The influence of helmet certification in motorcycle helmets protective performance

The convenience of online shopping has increased access to a vast array of helmet options and deals for motorcyclists. However, the e-commerce enables an influx of unverified and potentially hazardous helmets lacking the rigorous quality control into the market, hence, placing unaware bargain seekers at risk. The non-certified variants questions in terms of impact protection abilities because they visually look similar to certified helmets. This study compared certified full face and open face helmets against their non-certified counterparts by analysing injury predictor metrics. Using a test rig simulating 5.58 ± 0.29 m/s impacts, an anthropomorphic test device wearing both helmet types and certification statuses measured peak resultant linear and angular accelerations, head injury criterion alongside brain injury criteria scores. The data revealed comparable side and rear impact performance between non-certified and certified helmets. However, frontal impacts exposed deficiencies without certification. The non-certified full face helmets registered over twice the peak linear acceleration of certified while open face types still exceeded certified by 40% in frontal impacts. Additionally, non-certified full face helmets indicated up to 100% predicted concussion risks in side and frontal crashes based on the angular accelerations. The poorer frontal impact and elevated injury odds demonstrate certification's key safety advantages that certification should not be ignored while it still providing more protection than no helmet. However, individual needs to carefully select helmets due to performance differences of helmets. Riders should ultimately prioritize proven protection given the severe consequences of head trauma though non-certified may suffice for some low-risk environments

Predicting serious injuries due to road traffic accidents in Malaysia by means of artificial neural network

Malaysia has recorded a steady increase in the number of road traffic accidents from year to year at an alarming rate of 5%. Serious injuries due to the accidents, which could lead to permanent disability, might cause a long-term problem to the nation economy-wise. Predicting the number of serious injury cases in the future is important in understanding the trend of road traffic accidents to help policy-makers in proposing a countermeasure. Time-series model has been employed to predict the occurrence of road traffic crashes including fatalities. Nonetheless, the prediction of serious injury cases, which should not be taken lightly due to its potential impact, has not been proposed especially with regards to Malaysian road traffic accident data. This study attempts to employ Artificial Neural Networks (ANN), a machine learning algorithm, to predict the number of serious injury cases in Malaysia based on the road traffic accident data of the past 20 years. Ma-chine learning has increasingly been adopted in recent years owing to its ability to predict as well as catering for the non-linear behaviour of the data examined. A single-hidden ANN model was developed based on seven features, namely the number of registered vehicles, population, length of federal road, length of FELDA road, length of federal institutional road, length of federal territory road, and length of the expressway in order to predict the number of serious injuries. It was established from the present investigation that the developed ANN model is capable to predict the number of serious injuries from 1997 until 2017 with a mean absolute percentage error of only 3%. This demonstrates the capability of the developed machine learning in road traffic accident prediction, and it could be useful in outlining an action plan to mitigate the number of serious injuries in Malaysia

Forecasting road deaths in Malaysia using support vector machine

An average of 6,350 people died every year in Malaysia due to road traffic accidents. A published data of Malaysian road deaths in 20 years since 1997 reveals that the number of fatalities has not really declined with a difference of less than 10% from one year to the next. Forecasting the number of fatalities is beneficial in planning a counter measure to bring down the death toll. A predictive model of Malaysian road death has been developed using a time-series model known as auto regressive integrated moving average (ARIMA). The model was used in the previous Road Safety Plan of Malaysia to set a target death toll to be reduced in 2020, albeit being inaccurate. This study proposes a new approach in forecasting the road deaths, by means of a machine learning algorithm known as Support Vector Machine. The length of various types of road, number of registered vehicles and population were among the eight features used to develop the model. Comparison between the actual road deaths and the prediction demonstrates a good agreement, with a mean absolute percentage error of 2% and an R-squared value of 85%. The Linear kernel-based Support Vector Machine was found to be able to predict the road deaths in Malaysia with reasonable accuracy. The developed model could be used by relevant stakeholders in devising appropriate poli-cies and regulations to reduce road fatalities in Malaysia

A support vector machine approach in predicting road traffic mortality in Malaysia

Traffic mortality rate is the baseline through which road safety plans of a country could be evaluated. A reliable and reasonable analysis of road traffic related injuries and their leading causes is vital to the road safety investigation, evaluation as well as policymaking. Malaysia has the third highest fatality rate from road traffic accidents in Asia as well as in South East Asia. Although many researchers have attempted to provide predictive models of road traffic mortality (RTM) in the country, the predictions are found to be rather unsatisfactory in forecasting the causes as well as the future road fatality. It is hypothesized that the inability of the previous models to provide a good prediction of the RTM might be attributed to the complicated and non-linear data relationship of the underlying causes of road traffic accidents. A Support Vector Machine (SVM) is demonstrated to be effective in solving both classifications as well as regression problems owing to its efficacy to cater for the non-linear relationship of a dataset. The present investigation proposed the application of SVM based model variations namely; Linear, Quadratic, Cubic, Fine, Medium as well as Coarse Gaussian-based SVM in predicting the RTM. A dataset from 1972 to 1994 was obtained from the Malaysian road traffic database. The data were trained on the SVM model variations. It was demonstrated that the Linear based SVM model is able to provide a reasonable prediction of the RTM with only 12% error. It is, therefore, inferred that a reasonable prediction of RTM in Malaysia could be achieved through the employment of non-conventional statistical techniques